Applied Missing Data Analysis / Craig K. Enders ; Series Editor's Note by Todd D. Little.
| Author/creator | Enders, Craig K. |
| Format | Electronic |
| Edition | Second Edition. |
| Publication Info | New York : Guilford Publications, 2022. |
| Description | 1 online resource (564 pages). |
| Supplemental Content | Click here to view book |
| Subjects |
| Series | Methodology in the Social Sciences Ser Methodology in the Social Sciences Ser. UNAUTHORIZED |
| Contents | Cover -- Half Title Page -- Series Editor -- Title Page -- Copyright -- Series Editor's Note -- Preface -- Contents -- 1. Introduction to Missing Data -- 1.1 Chapter Overview -- 1.2 Missing Data Patterns -- 1.3 Missing Data Mechanisms -- 1.4 Diagnosing Missing Data Mechanisms -- 1.5 Auxiliary Variables -- 1.6 Analysis Example: Preparing for Missing Data Handling -- 1.7 Older Missing Data Methods -- 1.8 Comparing Missing Data Methods via Simulation -- 1.9 Planned Missing Data -- 1.10 Power Analyses for Planned Missingness Designs -- 1.11 Summary and Recommended Readings |
| Contents | 2. Maximum Likelihood Estimation -- 2.1 Chapter Overview -- 2.2 Probability Distributions versus Likelihood Functions -- 2.3 The Univariate Normal Distribution -- 2.4 Estimating Unknown Parameters -- 2.5 Getting an Analytic Solution -- 2.6 Estimating Standard Errors -- 2.7 Information Matrix and Parameter Covariance Matrix -- 2.8 Alternative Approaches to Estimating Standard Errors -- 2.9 Iterative Optimization Algorithms -- 2.10 Linear Regression -- 2.11 Significance Tests -- 2.12 Multivariate Normal Data -- 2.13 Categorical Outcomes: Logistic and Probit Regression |
| Contents | 2.14 Summary and Recommended Readings -- 3. Maximum Likelihood Estimation with Missing Data -- 3.1 Chapter Overview -- 3.2 The Multivariate Normal Distribution Revisited -- 3.3 How Do Incomplete Data Records Help? -- 3.4 Standard Errors with Incomplete Data -- 3.5 The Expectation Maximization Algorithm -- 3.6 Linear Regression -- 3.7 Significance Testing -- 3.8 Interaction Effects -- 3.9 Curvilinear Effects -- 3.10 Auxiliary Variables -- 3.11 Categorical Outcomes -- 3.12 Summary and Recommended Readings -- 4. Bayesian Estimation -- 4.1 Chapter Overview |
| Contents | 4.2 What Makes Bayesian Statistics Different? -- 4.3 Conceptual Overview of Bayesian Estimation -- 4.4 Bayes' Theorem -- 4.5 The Univariate Normal Distribution -- 4.6 MCMC Estimation with the Gibbs Sampler -- 4.7 Estimating the Mean and Variance with MCMC -- 4.8 Linear Regression -- 4.9 Assessing Convergence of the Gibbs Sampler -- 4.10 Multivariate Normal Data -- 4.11 Summary and Recommended Readings -- 5. Bayesian Estimation with Missing Data -- 5.1 Chapter Overview -- 5.2 Imputing an Incomplete Outcome Variable -- 5.3 Linear Regression -- 5.4 Interaction Effects -- 5.5 Inspecting Imputations |
| Contents | 5.6 The Metropolis-Hastings Algorithm -- 5.7 Curvilinear Effects -- 5.8 Auxiliary Variables -- 5.9 Multivariate Normal Data -- 5.10 Summary and Recommended Readings -- 6. Bayesian Estimation for Categorical Variables -- 6.1 Chapter Overview -- 6.2 Latent Response Formulation for Categorical Variables -- 6.3 Regression with a Binary Outcome -- 6.4 Regression with an Ordinal Outcome -- 6.5 Binary and Ordinal Predictor Variables -- 6.6 Latent Response Formulation for Nominal Variables -- 6.7 Regression with a Nominal Outcome -- 6.8 Nominal Predictor Variables -- 6.9 Logistic Regression |
| General note | Description based upon print version of record. |
| General note | 6.10 Summary and Recommended Readings |
| Source of description | Print version record. |
| Issued in other form | Print version: Enders, Craig K. Applied Missing Data Analysis, Second Edition. 2nd ed. New York : Guilford Publications, 2022 9781462550005 |
| ISBN | 9781462550005 (electronic bk.) |
| ISBN | 1462550002 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |